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Comparative analysis of three regression methods for the winter wheat biomass estimation using hyperspectral measurements

文献类型： 会议论文

第一作者： Xingang Xu

作者： Xingang Xu 1 ; Yuanyuan Fu 1 ; Guijun Yang 1 ; Haikuan Feng 1 ; Xiaoyu Song 1 ; Jihua Wang 1 ;

作者机构： 1.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences

关键词： Winter wheat biomass;Hyperspectral;Partial least squares regression;Principal component regression;Stepwise multiple linear regression;Spectral transformation

会议名称： International Conference on Computer Science and Electronics Engineering

主办单位：

页码： 1733-1736

摘要： Hyperspectal data contain more useful information for characterizing vegetation biomass, compared with multi-spectral data. However, to make full use of the hyperspectral data, the strong multi-collinearity in the data is supposed to be taken into account. With this study we evaluated three multivariate regression methods which are principal component regression (PCR), partial least square regression (PLSR) and stepwise multiple linear regression (SMLR). They are specifically designed to deal with multi-collinearity problem. Furthermore, to identify reliable winter wheat biomass predictive models different types of spectral transformations (continuum removal, first derivative) were combined with the three regression methods, respectively. The comparative analysis was conducted on the data sets collected in 2008 and 2009 field campaigns in Tongzhou and Shunyi district, Beijing, China. Compared with the other combination, the respective combination of three regression methods and continuum removal got the highest estimation accuracy, especially, the combination of PLSR and continuum removal (R~2=0.715, RMSE=0.218 kg/m~2). The experimental results demonstrated that the use of PLSR is recommended for highly multi-collinear data sets. The combination of continuum removal and PLSR could improve the estimation accuracy of winter wheat biomass.

分类号： tp3-53

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